Presentations for the general public

Monday 06 July

Time Activity
15:00 Modelling biological systems: towards digital twins
Prof. Ilja Arts | Scientific director MaCSBio
15:30 Modeling the human brain to study hearing
Dr. Michelle Moerel | Assistant Professor MaCSBio
16:00 Systems Biology vs. zombie cells: Tackling senescent cells in human aging
Dr. Marian Breuer | Assistant Professor MaCSBio

Descriptions

Modelling biological systems: towards digital twins
Prof. Ilja Arts
Digital version of ourselves are no longer science fiction. At this moment, scientists all over the world are building computer models that can be used to simulate the effects of treatment of an individual patient, or to better understand how biological systems like our brain work. Systems biology is a combination of biology and mathematics that tackles complex problems in a holistic way. I will introduce the concept of systems biology and give some examples of its manifold applications.

Modeling the human brain to study hearing
Dr. Michelle Moerel
How do we follow a conversation in a noisy environment? How do we recognize speech irrespective of the speaker’s accent? The human brain, arguably the most complex biological system there is, effortlessly achieves these tasks. I will discuss how brain imaging combined with computational modeling can shed light on human hearing.

Systems Biology vs. zombie cells: Tackling senescent cells in human aging
Dr. Marian Breuer
Cells that are aged, damaged or in danger of turning to cancer cells can become "senescent". In this state they cannot divide anymore but can flood their surroundings with harmful secretions. These "zombie" cells accumulate more and more as a person ages, can damage surrounding cells and can even make them senescent too. How can systems biology help tackling these cellular zombies and their role in human aging and age-related diseases?

Master Systems Biology selected students presentations

Tuesday 07 July

Time Activity
15:00 Opening
Dr. Julia Massimelli Sewall | Director of master's programmes Sciences
15:05 Predicting the effect of single-point missense variants in the binding site on protein-ligand binding affinities: A machine learning approach
Ammar Ammar | Systems Biology student
15:20 The visual cortex as a network of phase-oscillators
Kris Evers | Systems Biology student
15:35 Assessing outcomes and risk factors of atrial fibrillation through a lifetime population-level markov model
Cristian Barrios Espinosa | Systems Biology student
15:50 Towards understanding complex behaviour in goal-oriented systems
Raphael Stolpe | Systems Biology student

Descriptions

Predicting the effect of single-point missense variants in the binding site on protein-ligand binding affinities: A machine learning approach
A. Ammar
A key concept in drug design is how natural variants, especially the ones occurring in the binding sites of drug targets, affect the inter-individual drug responses and efficacies by altering binding affinities. These effects have been reported in literature on very limited and small datasets that are not suitable for machine learning prediction models. Ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed to build a machine learning (ML) model predicting these effects.

However, to the best of our knowledge, such a dataset did not exist yet. To solve this, a reference dataset of ligands binding affinities to proteins with all their reported binding site variants was constructed using a molecular docking approach. A numerical vector representation of protein, binding pocket, mutation, and ligand information was encoded using a total of 256 extracted features to describe the protein-ligand pair. Using this dataset, two designs of machine learning regression models were trained and evaluated on the chosen features to predict the binding affinity, and six different scenarios to split training and test data were evaluated. The models trained on datasets based on ligand molecular weight split reported the best performance. The best performance model reported an RMSE value of 0.57 kcal/mol-1 on an independent test set with an R-squared value of 0.86 and a prediction speed of 29 bound compounds affinities per second. We report an improvement in the protein-ligand binding affinity prediction performance of the ML models compared to several published models. The obtained models can be used in early-stage drug discovery to rapidly and accurately obtain a better overview of the ligand binding affinity variability across genetic variants. This may benefit applicability of medicine across ethnic groups as well as in personalized medicine.

The Visual Cortex as a Network of Phase-Oscillators
K.S. Evers
The human brain is capable of extracting information from visual scenes. Mathematical models of the visual system can be used to link biological structure to function. Neurons in the early visual cortex are sensitive to oriented stimuli, and local synaptic connections are biased towards neurons with collinear receptive fields. This suggests a role for lateral connections in the early visual cortex in contour integration. Human psychophysical experiments (Field et al., 1993) suggest that a local association field is expressed by the visual system. This local association field suggests a relatively high association between collinear and proximal oriented stimuli.

The higher association between collinear stimuli allows for grouping of smooth contours. In this project the local association field is implemented in a phase-oscillator network model assuming binding-by-synchrony. The phase-oscillators represent neuron populations of the early visual cortex, the lateral connections between the oscillators are based on the local association field theory, and grouping of contours is assumed to take place through synchrony between phase-oscillators. The frequency- and phase code can be used by the brain to interpret a visual scene. Experiments are performed by simulating the phase update in the Kuramoto model. The first experiment confirms that the model is capable of performing contour integration through synchrony. New experiments are designed to generate testable predictions and hypotheses. These predictions, hypotheses and future experiments and extensions of the model are discussed.

Assessing Outcomes and Risk Factors Of Atrial Fibrillation Through A Lifetime Population-Level Markov Model
Cristian Alberto Barrios Espinosa
Atrial fibrillation (AF) is characterized by abnormal electrical and mechanical activity of the atria and presents a major global health burden. Computational models are increasingly being used to study AF and guide its treatment. However, current computational models and epidemiological studies are unable to bridge the gap of knowledge between mechanisms of AF pathophysiology and long-term clinical outcomes.

Aim: To develop a novel computational model to bridge this knowledge gap by simulating a virtual population of patients over a lifetime period. Methods: A Markov model with 5 states for combinations of AF, sleep-disordered breathing (SDB) and death was developed. The stochastic transitions between states are controlled by clinical risk factors (age, sex, SDB) and mechanisms of AF pathophysiology (shortening of effective refractory period and slowing of conduction velocity). Results: The model was calibrated to reproduce real-world data with respect to: 1) epidemiology of AF and SDB, 2) mortality in the general population and in AF patients, 3) patterns of AF paroxysms, and 4) relation between clinical subtypes of AF and atrial fibrosis. Finally, the calibrated model was used to analyze the potential therapeutic effects on AF of different types of risk-factor management for SDB. Conclusion: We successfully developed a model that connects mechanistic and epidemiological research in AF and show how this model can be used to perform a ‘virtual clinical trial’ to generate new hypotheses that can be used to guide new clinical research and ultimately lead to better AF management. Impact: This novel patient-level model will be used to study the effect of new policies on the burden caused by AF.

Towards understanding complex behaviour in goal-oriented systems
PR Stolpe
Understanding complex, goal-oriented behaviour remains an ongoing challenge in neuroscience. Meaningful progress has been made through intelligently designed experiments. Visual and sensory information processing as well as action generation were found to be a function of the frontoparietal network spanning the visual, sensory and motor cortices. Computational neuroscientists successfully devised models to explain neural phenomena encountered in the frontoparietal network. However, unlike other natural sciences computational neuroscience must not only account for neural phenomena, but also address information processing in the brain. Therefore, an ideal model must be capable of generating complex, goal-oriented behaviour while also accounting for neural phenomena. Dynamical systems have been widely applied to model neural dynamics, but also to generate control policies in robotics. This work proposes to leverage computing with dynamical systems to understand neural dynamics and complex behaviour expressed as control policies in goal-oriented systems.

To that end, this work utilises recent advances in deep reinforcement learning. Agents are trained to solve a variety of reinforcement learning problems including control of an anthropomorphic hand. Afterwards, dynamics recorded while the agent performed the task are critically assessed. Attractor networks and central pattern generators are found as general computational strategies to solve for final state goals and periodic tasks (e.g. walking), respectively. These findings demonstrate the capability of computing with dynamical systems to account for neural dynamics and information processing. Computing with dynamical systems when applied to more complex tasks could pave the way to generating ever more sophisticated hypotheses about complex behaviour in artificial and biological systems.

Keynote and PhD alumni: Computational Neuro-Genetics​

Wednesday 08 July

Time Activity
15:00 Opening
Prof. Elia Formisano | Research line leader MaCSBio
15:05 Keynote: Translational Neuromodeling, Computational Psychiatry and Computational Psychosomatics
Prof. dr. Klaas Enno Stephan | Director Translational Neuromodeling Unit, University of Zurich, Switzerland
15:50 Predicting neuronal response properties from hemodynamic responses in the auditory cortex
Isma Zulfiqar | PhD alumnus MaCSBio, Researcher, Maastricht University
16:20 The Behavioral Arnold Tongue and the Perception and Learning of Figure-ground Distinction
Maryam Karimian | PhD alumnus MaCSBio, Researcher, Maastricht University

Translational Neuromodeling, Computational Psychiatry and Computational Psychosomatics
Klaas Enno Stephan | Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich
For many brain diseases, particularly in psychiatry, we lack clinical tests for differential diagnosis and cannot predict optimal treatment for individual patients. This presentation outlines a translational neuromodeling framework for inferring subject-specific mechanisms of brain disease from non-invasive measures of behaviour and neuronal activity. Guided by clinical theories of maladaptive cognition and aberrant brain-body interactions, generative models can be developed that have potential as “computational assays”.

Evaluating the clinical utility of these assays requires prospective patient studies that address concrete clinical problems, such as treatment response prediction. If successful, computational assays may help provide a formal basis for differential diagnosis and treatment predictions in individual patients and, ultimately, facilitate the construction of mechanistically interpretable disease classifications.

Predicting neuronal response properties from hemodynamic responses in the auditory cortex
Isma Zulfiqar | PhD alumnus MaCSBio, Researcher, Maastricht University

The differences in neuronal response properties between the rostral and caudal streams in the auditory cortex are thought to support the specialized functions of ‘what’ and ‘where’ (or ‘how’) processing, respectively. While the responses in rostral and caudal auditory belt regions have also been examined in the human auditory cortex using fMRI, it has been challenging to relate observed differences in fMRI responses along the rostral-caudal axis of the human temporal lobe to fundamental neuronal response properties.

To bridge this gap, we presented a forward model combination of neuronal model of the auditory cortex with physiological model of hemodynamic BOLD response. Our simulations showed that the hemodynamic BOLD responses of the caudal belt regions in the human auditory cortex were best explained by modeling faster temporal dynamics and broader spectral tuning of neuronal populations, while rostral belt regions were best explained through fine spectral tuning combined with slower temporal dynamics. These results suggest two parallel streams of complimentary information processing in human auditory cortex.

The Behavioral Arnold Tongue and the Perception and Learning of Figure-ground Distinction
Maryam Karimian

We combined psychophysical experimentation with Kuramoto phase oscillator modeling approaches to investigate the role of neural gamma oscillations in figure-ground distinction in visual images. Thus, a set of stimuli constituted from Gabor annuli is presented to a group of human subjects in the experiments and also, used as the input for the computational model. Our main hypotheses are as follows:

1- The likelihood of figure-ground distinction would be a function of contrast variance and spacing scale between the Gabor annuli in the stimuli. That is investigated by constructing the behavioral Arnold tongue.

2- Through learning, the likelihood of figure-ground distinction would increase. In other words, the region in the behavioral Arnold tongue that indicates highly probable figure-ground distinction would grow.

3- Perceptual learning is specific for the position of the figure in the visual field. That means our findings would support the idea of bottom-up learning, which involves plastic changes in lower-level cortical areas such as V1.

Workshops

Thurday 09 July

Time Activity
15:00
  • Workshop 1: Beginner’s guide to metabolic networks: From modelling to visualization
    Chaitra Sarathy | PhD candidate, MaCSBio
  • Workshop 2: Network analysis in biomedical research using Cytoscape
    Dr. Martina Kutmon | Assistant Professor, MaCSBio
  • Workshop 3: Using deep neural network models to unravel neural sensory processing: Sound encoding in the human brain
    Dr. Kiki van der Heijden | Postdoc, Radboud University, Nijmegen

Keynote and PhD alumni: Systems Medicine of Chronic Diseases

Friday 10 July

Time Activity
15:00 Opening
Prof. Ilja Arts | Scientific director MaCSBio
15:05 Keynote presentation 2
Prof. Natasa Pržulj | Professor of Biomedical Data Science at Computer Science, University College London, and ICREA Research Professor at Life Sciences Department, Barcelona Supercomputing Center
15:50 Stratifying cellular metabolism during weight loss
Dr. Samar Tareen | PhD alumnus MaCSBio, Postdoc, The Brabaham Institute, Cambridge, UK
16:20 A computational model of postprandial adipose tissue lipid metabolism
Shauna O’Donovan | PhD alumnus MaCSBio, Researcher Postdoc Wageningen University
  • Presentations for the general public

    Monday 06 July

  • Master Systems Biology selected students presentations

    Tuesday 07 July

  • Keynote and PhD alumni: Computational Neuro-Genetics​

    Wednesday 08 July

  • Workshops

    Thurday 09 July

  • Keynote and PhD alumni: Systems Medicine of Chronic Diseases

    Friday 10 July